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10.3390/genes8100246 http://scihub22266oqcxt.onion/10.3390/genes8100246 C5664096!5664096!28956817     free     free     free Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28956817.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Genes+(Basel) 2017 ; 8 (10): ä Nephropedia Template TP {{tp|p=28956817|t=2017. Pathway Enrichment Analysis with Networks |pdf=|usr=}}{{28956817}} gab.com Text Pathway Enrichment Analysis with Networks JO: Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28956817 #FOAvip Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA?, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene?gene interactions. Both algorithms utilize a protein?protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments. Twit Text FOAVip Pathway Enrichment Analysis with Networks ????Genes (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28956817 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28956817 #FOAvip English Wikipedia <ref>{{Cite pmid|28956817}}</ref> Pathway Enrichment Analysis with Networks #MMPMID28956817 Liu L; Wei J; Ruan J Genes (Basel) 2017[Oct]; 8 (10): ä PMID28956817show ga Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA?, for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene?gene interactions. Both algorithms utilize a protein?protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments. äPathway Enrichment Analysis with Networks (epmc).pdf DeepDyve Pubget Overpricing